latin american economic outlook 2015 - unesco · (e.g. soft skills, creativity) • bridges with...

UNESCO Regional Forum on TVET

Montevideo, Uruguay. 23-25 November 2015

Latin American Economic Outlook 2015 Education, skills and innovation

Rolando Avendano OECD Development Centre

Perspectivas económicas de América Latina

1 Skills, shifting wealth and the middle-income trap

Education and inclusive growth 2

Looking forward: challenges to 2030 3

Education and skills in Latin America: an OECD perspective

3

The middle income trap has proven to be a persistent event in Latin

America

Evading the middle income trap in Latin America (GDP per capita; 1990 USD PPP)

Source: OECD/CAF/ECLAC calculations based on the methodology proposed by Felipe, Abdon and Kumar (2012).

Data extracted from International Monetary Fund, World Economic Outlook database (2015) and Maddison (2010) database

Low

Middle

High

0

5000

10000

15000

20000

25000

30000

35000

CHL URY ARG VEN CRI MEX COL BRA PER CHN SGP JPN KOR ESP PRT MYS

2014 1980 1950

4

Source: World Bank Enterprise Survey (2014)

Proportion of firms that consider the lack of labour force with the adequate skills a significant restriction to growth (% formal firms)

Latin America is characterized by a large skill gap

Employment and occupations in LAC tend to be low-skilled, in stark

contrast to OECD countries

Source: Own elaboracion using ILO Key Indicators of the Labour Market Database 5

Low skills

(education and task)

Employment and occupations in LAC tend to be low-skilled, in stark

contrast to OECD countries (II)

Source: Own elaboracion using ILO Key Indicators of the Labour Market Database 6

High skills

(education and task)

7

Source: De la Torre et al. (2013).

Despite the unmet demand for skills…returns to education and skills have been falling

Latin America is characterized by a large skill gap

1,4

1,5

1,6

1,7

1,8

1,9

2

2,1

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Relative wages

Secundaria/primaria Terciaria/secundaria



Education and inclusive growth: are vocational schools concerned? 2



9

Education is a critical vector of social cohesion and inclusive growth

Source: Own calculations based on World bank indicators and WEF.

Quality of education and labour productivity: partial correlations

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

OECD

correlation coefficient= 0.66

Quality of superior education explained by GDP per capita

Labour productivity not explained by GDP per capita

-60000

-50000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

-3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

Others LAC OECD

correlation coefficient= 0.66

Quality of superior education explained by GDP per capita

Labour productivity not explained by GDP per capita

Source: Own elaboration using OECD/PISA 2012 data 10

Latin America lags behind in terms of performance and equity

Education performance and equity in education

CHLMEX

ARG

BRA

COL

CRI

PER

URY

Others

OECD

LA

350

400

450

500

550

600

650

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Performance in math, PISA points 2012

Percentage of the variation on the performance explained by socio-economic status from the student and school

HKG

MAC

CAN

CHL

ESTFIN

KOR

MEX

POL

ARG

BRA

COL

CRI

PER

URY

Others

OECD

LA

350

400

450

500

550

600

650

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Performance in math, PISA points 2012

Percentage of the variation on the performance explained by socio-economic status from the student and school

Public expenditure on education over GDP has nearly converged, but

remains lower in GDP pc terms per student

11 Source: UNESCO Institute for Statistics

Public expenditure per student (% of GDP per capita)

Improvements in investment and enrolment (primary and secondary)

during the last 20 years are remarkable

Source: UNESCO Institute for Statistics 12

Enrolment rate by education level (%, circa 2011)

0

10

20

30

40

50

60

70

80

90

100

Primary

Primary (net rate, %)

0

10

20

30

40

50

60

70

80

90

100

Pre-school &

Pre-school (net rate, %) Primary (net rate, %)

0

10

20

30

40

50

60

70

80

90

100

Secondary & Tertiary

Secondary (net rate, %) Tertiary (gross rate, %)

Expenditure and enrolment gaps are evident in pre-primary education,

which will impact future performance

13 Source: Own elaboration using OECD/PISA 2012 data

Gains from pre-primary education on secondary education (%) (2012, Percentile of Performance)

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

OECD COL MEX CRI BRA AL URY ARG CHL PER

Percentile 10 Percentile 50 Percentile 90


Socioeconomic background and quality of resources in the school (2012, value between 0=no impact and 1=full impact)

There are significant differences in terms of performance due to

socio-economic background


Private vs. Public Schools before and after controlling for socio-economic status (PISA points)

How important is the net value-added of private schools?

-60

-40

-20

0

20

40

60

80

100

120

140

Others OECD MEX COL CHL ARG LA CRI PER BRA URY

Private Private after controlling for Socio-Economic Status of students Private after controlling for Socio-Economic Status of students and schools


Location and cultural background affects education performance: the case of Peru (PISA points)

There are significant differences in terms of performance due

to rural/urban and cultural origin

250

300

350

400

450

500

550

600

Max OECD KOR

Average OECD

CHL Min OECD MEX

URY CRI LAC BRA ARG COL PER Spanish

PER Total

PER Quechua

17

Source: Own elaboration using OECD/PISA 2012 data

Key to translate motivation into efficacy in Latin America (in particular for girls in mathematics performance)

There are significant differences in terms of performance

due to gender imbalances

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

Others OECD LA

Intrinsic motivation in learning mathematics index

Girls Boys

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Others OECD LA

Self-efficacy in mathematicsindex

Girls Boys

300

320

340

360

380

400

420

COL ARG CRI CHL MEX

Socioeconomically disadvantaged, Latam

Gene

300

320

340

360

380

400

420

440

460

480

COL ARG MEX CRI CHL

Socioeconomically advantaged, Latam

General

Vocacional

300

350

400

450

500

550

MNE ITA BEL SVN HRV

Socioeconomically disadvantaged, OECD and Others

300

350

400

450

500

550

600

HRV BEL ITA MNE SVN

Socioeconomically advantaged, OECD and Others

General

Vocational

A regional puzzle: LAC’s performance of vocational

schools above general schools

LAC students in vocational programmes are more

motivated than in OECD

Source: Authors’ calculation, based on PISA database 2012.

-0,4

-0,3

-0,2

-0,1

0,0

0,1

0,2

0,3

0,4

0,5

GEN VOC GEN VOC GEN VOC GEN VOC

Instrumental motivation Intrinsic motivation Self-efficacy Self-concept

Instrumental and Intrinsic motivation, self efficacy an self-concept

Others

OECD

Latam

LAC students in vocational programmes are also

more perseverant


-0,3

-0,2

-0,1

0,0

0,1

0,2

0,3

0,4

0,5

GEN VOC GEN VOC GEN VOC GEN VOC

Perseverance Openness Failure perc. Willingness

Perseverance, Openness to problem solving, Perceived Failure in Maths, and Maths intentions

Others

OECD

Latam

Vocational and general schools have similar

teaching and educational endowments


0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

GEN VOC GEN VOC

Prop. Certified teachers Prop. with ISCED5A qualification

Proportion of certified teachers and teachers with ISCED5A qualification

Others OECD Latam

-1,0

-0,8

-0,6

-0,4

-0,2

0,0

0,2

GEN VOC GEN VOC

Index Educational resources Index physical infrastructure

Education resources and physical infrastructures

Others

OECD

Latam

Explaining the gap: school-based factors

XESCS Economic, social and cultural status of the school

Clsize Class size

School status Private or public school

Autonomy - program Autonomy of the school to establish curriculum

Autonomy - budget Autonomy of the school to define budget allocation

Infrastructure Index of physical infrastructure

Educational resources Index of educational resources of the school

Teachers' certification Proportion of teachers with certification from corr.

Authority

Teachers' qualification Proportion of teachers with qualification

Tutoring Dummy for mathematics lessons in school hours

Feedback Frequency of feedback from principal to teachers

Additional classes Dummy if additional classes > 2h per week

Instructional time Weekly instructional time in Maths

Culture and expectations Dummy if consensus that academic achievement must

be kept high

Vocational schools in Latin America: why do they

perform better?

• Avendano, Nieto-Parra, Nopo and Vever (2015)

• School-based factors explain little of the difference in performance between

vocational and general schools

• A number of student-based factors seem to play a larger role in explaining these

differences:

• Relative grade: PISA target + late selection age in LAC

• Motivation and perseverance: student commitment

• Vocational track “triggers” better performance among students from low SES and

students in rural areas. Instruments for social mobility?



Education and inclusive growth 2



A Conceptual Framework

Channels

Rebalancing

Scenarios China’s Trends

Older, richer,

closer

Struct.transf.

& services

Trade

Skills

Export profile

Competition

Investment

FDI

Loans

“Going out”

policy

Baseline vs

low growth

High vs low

skill catch-up

Targeted vs

diversified Inv.

Policies

Skills strategy

Towards “new”

PDPs

Regulatory

Framework

Financing gap

Perspectivas económicas de América Latina Beyond quantity: quality, pertinence and matching with the

economy

Performance on PISA tests - China vs Latin America

Source: PISA 2009 dataset and OECD (2015).

China’s skill strategy to 2030:

• Improving quality in education (pre-primary coverage, teaching incentives)

• Focus on service-related skills (e.g. soft skills, creativity)

• Bridges with labour market (workplace training, vocational education)

Note: The ranking of countries and provinces is according to the reading score. Vocational schools are included except in “Zhejiang-China ” and “China 11

provinces”. China’s sample includes 21 003 pupils from 621 schools in 11 provinces and municipalities


Share of tertiary educated global population (25+): 2010-2030

Note: India not included owing to lack of data. "Source: OECD/CAF/ECLAC calculations based on World Bank (2015a), World Development Indicators, http://data. worldbank.org/data-catalog/world-development-indicators and UNESCO Institute for Statistics, www.uis. unesco.org/Pages/default.aspx.”

LAC’s global share of high skills will decline, while China will

supply 1 out of 4 tertiary educated individuals by 2030

13%

9%

57%

21%

2010

LAC China OECD RoW

11%

20%

45%

24%

2020

LAC China OECD RoW

10%

23%

42%

25%

2030

LAC China OECD RoW


Population with tertiary education: projections 2013 to 2030, China and LAC

Source: Author's calculations based on World Bank (2015), World Development Indicators and UNESCO Institute for Statistics.

By 2030, China will have more than 200 million tertiary educated

individuals, compared to LAC’s 100 million

0

50.000.000

100.000.000

150.000.000

200.000.000

250.000.000

300.000.000

20

13

20

14

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

LAC (high graduation) LAC (baseline) China (high graduation) China (baseline)

Perspectivas económicas de América Latina Skills composition: STEM focus on China’s skills strategy

Tertiary educated students by field of education

47,8%

17,3%

23,5%

62,5%

28,7% 20,2%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

China LAC

STEM Humanities, social sciences, law and education Other

Sources: EdStats World Bank for LAC; National Bureau of Statistics for China

30

Conclusions and strategic policy areas

- Upgrade skills, including those more directly related to jobs

- Better matching of demand and supply of skills.

- Better understanding of training and skills needs.

- Some lessons:

- Align skills strategy to national development plan (Korea)

- Qualifications framework for certain sectors (Chile)

- Workplace trainings/dual system (Germany, China)

3- El liderazgo de China

• Increase coverage in pre-primary education to obtain long term gains in

terms of both soft and cognitive skills

• Implement cost-effective policies at the classroom level (tutoring,

instructional time, teachers’ expectations on students) to improve students’

performance

• Improve the assessment and monitoring mechanisms (teachers’

accreditation in secondary, accreditation in tertiary)

• Invest more and better in areas that could reduce inequalities at income

and gender levels (violence at school, CCTs and performance,

heterogeneity at school according to socio economic status)

31

Some policy implications: improving quality


Thank you!

www.oecd.org/dev

http://www.oecd.org/dev

China’s new normal: export re-composition for LAC

• While avg. growth rate in 2000s was 10.5%, by 2030 China’s growth will reach between 5.5% and 7.3% (WB-DRC, 2013).

• Investment at its peak (49% of GDP for 1995-2010). Investment ratios will fall to 34% for 2030. Consumption will steadily increase from 47% (1995-2010) up to 66%.

• Chinese food consumption patterns will move from staple crops towards more caloric and protein-based foods (Von Lampe, 2015). Demand for services (logistics, financial, education, health) will also increase.

• Threats and opportunities. While some exports can face downward pressures, sophisticated agricultural products and services may experience a boosting future demand.

• Our analysis:

(1) Clustering (2) Export patterns (3) Projections

China will shift its Non-agricultural consumption from

manufactures and extractive sectors towards Services.

-3,0%

-2,5%

-2,0%

-1,5%

-1,0%

-0,5%

0,0%

"Globalization" Scenario "Separate Growth" Scenario

China's consumption of manufactures 2030

Max Mean Min

-3,0%

-2,5%

-2,0%

-1,5%

-1,0%

-0,5%

0,0%


China's consumption of Extractive Sector products 2030

Max Mean Min

0,0%

0,5%

1,0%

1,5%

2,0%

2,5%

3,0%

3,5%

4,0%

4,5%


China's consumption of Services 2030

Max Mean Min

Note: Change in share of Non-Agricultural product’s consumption from 2010, % points

Source: Based on Von Lampe (2015). Calculations in the change of shares by authors (results to be validated by OECD modelers).

For Latin America’s exports, extractive sectors will still play a major

role, with ambiguous changes in services and manufactures.

-2%

0%

2%

4%

6%

8%


Mexico's share of manufactures exports 2030

Max Mean Min

-2%

0%

2%

4%

6%

8%


Latin America's share of extractive exports 2030

Max Mean Min

-2%

0%

2%

4%

6%

8%


Latin America's share of manufactures exports 2030

Max Mean Min

-2%

0%

2%

4%

6%

8%


Latin America's share of Services exports 2030

Max Mean Min

* Change in share of product exports from 2010, % points

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